model-registry

What This Skill Does

The Model Registry skill serves as the central nervous system for your machine learning operations within the OpenClaw environment. It provides an enterprise-grade framework for tracking, versioning, and lifecycle management of ML models. By integrating directly with SpecWeave's increment workflow, the skill ensures that every model version is cryptographically linked to its training data, hyperparameter configurations, and environment dependencies. It automates the transition of models through strictly defined stages: Development, Staging, Production, and Archived. This structure prevents common pitfalls such as lost model lineage, inability to reproduce legacy results, or accidental deployment of untested algorithms into production environments.

Installation

To integrate this skill into your OpenClaw agent, execute the following command in your terminal: clawhub install openclaw/skills/skills/anton-abyzov/sw-model-registry After installation, ensure that your SpecWeave environment is initialized, as the registry relies on the underlying increment tracking system for version persistence.

Use Cases

• Production Auditing: When compliance requires a report on which version of a model was active during a specific financial transaction window.
• Risk Mitigation: Utilizing the one-command rollback feature to instantly revert to a stable model version if a new deployment shows performance degradation.
• Cross-Team Collaboration: Enabling data scientists and ML engineers to share validated models from the Staging environment with clear metadata and feature schemas.
• Lifecycle Enforcement: Automatically preventing models that haven't passed the validation gate from reaching the Production environment.

Example Prompts

1. "OpenClaw, check the status of the fraud-detection-model. Which version is currently in production and when was it last updated?"
2. "Promote the latest version of the customer-churn-model from staging to production, ensuring all metadata is correctly logged."
3. "I need to rollback the recommendation-engine to version v2.4.0 due to recent latency issues; please initiate the revert process."

Tips & Limitations

• Versioning Strategy: Stick to semantic versioning (major.minor.patch) to clearly communicate the impact of changes to stakeholders.
• Metadata Integrity: Always provide comprehensive metadata (features, framework, training_date) when registering a new model, as this is essential for long-term reproducibility.
• Limitations: This skill does not perform model training itself; it assumes that the training process has already been executed elsewhere and that you are registering the resulting artifacts.